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Communicating Narrative Concerns Entered by RNs (CONCERN)
Investigator (PI): Rosetti, Sarah Collins; Cato, Kenrick Dwain
Performing Organization (PO): (Current): Columbia University Irving Medical Center, Department of Medicine, Division of General Medicine / (212) 305-5712
Supporting Agency (SA): National Institutes of Health (NIH), National Institute of Nursing Research (NINR)
Initial Year: 2017
Final Year: 2023
Record Source/Award ID: RePorter/R01NR016941
Funding: 2017 Award Amount: $516,245
2018 Award Amount: $658,716
2019 Award Amount: $646,418
2020 Award Amount: $595,773
Award Type: Grant
Award Information: Reports resulting from this project
Abstract: Annually, more than 200,000 patients die in U.S. hospitals from cardiac arrest and over 130,000 patient inpatients deaths are attributed to sepsis. These deaths are preventable if patients who are at risk are detected earlier. Our prior work found that nursing documentation within electronic health records (EHRs) contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients. We defined a new source of predictive data by analyzing the frequency and types of nursing documentation that indicated nurses' increased surveillance and level of concern for a patient. These data, documented in the 48 hours preceding a cardiac arrest and hospital mortality, were predictive of the event. While clinicians strive to provide the best care, there is a systematic problem within hospital settings of non-optimal communication between nurses and doctors leading to delays in care for patients at risk. Well designed and tested EHRs are able to trend data and support communication and decision making, but too often fall short of these goals and actually increase clinician cognitive load through fragmented information displays, "note bloat," and information overload. Substitutable Medical Applications & Reusable Technologies (SMARTapps) using Fast Health Interoperability Resource (FHIR) standard allow for open sharing and use of innovations across EHR systems. The aim of this project is to design and evaluate a SMARTapp on FHIR used across two large academic medical centers that exposes physicians and nurses to our new predictive data source from nursing documentation to increase care team situational awareness of at-risk patients to decrease preventable adverse outcomes. The SMARTapp we will design and evaluate is the Communicating Narrative Concerns Entered by RNs (CONCERN) clinical decision support (CDS) system. This will be integrated at four hospitals part of two health systems, Brigham and Women's Hospital (BWH) and Newton Wellesley Hospital (NWH), part of Partners Healthcare System (PHS) in Boston, and New York-Presbyterian Hospital-Columbia University Medical Center (NYP-CUMC) and The Allen Hospital, part of New York Presbyterian Health System (NYP) in New York. Specifically, we will 1) validate desired thresholds for the CONCERN SMARTapp, 2) integrate the CONCERN SMARTapp for early warning of risky patient states within CDS tools, and 3) evaluate the CONCERN SMARTapp on primary outcomes of in-hospital mortality and length of stay and secondary outcomes of cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates. The methods we will use include data mining and natural language processing, factorial design surveys, simulation testing for evaluating team-based situational awareness, and outcomes evaluation in the medical intensive care units and acute care units (non-ICU) at our study sites.
MeSH Terms:
  • Academic Medical Centers
  • Boston
  • Cardiology /methods
  • Communication
  • Decision Making
  • * Decision Support Systems, Clinical
  • Electronic Health Records
  • Heart Arrest /*nursing
  • Hospitalization
  • Humans
  • Intensive Care Units
  • Length of Stay
  • * Mobile Applications
  • New York City
  • Nursing /*methods
  • Patient Readmission
  • Physician-Nurse Relations
  • Risk
  • Time-to-Treatment
  • United States
Keywords:
  • Boston
  • New York
  • academic medical centers
  • adverse outcome
  • awareness
  • cardiac death
  • cessation of life
  • clinical decision support
  • clinical decision support systems
  • communication
  • critical care
  • data
  • data mining
  • decision making
  • discipline of nursing
  • documentation
  • early identification
  • electronic health record
  • evaluation
  • heart arrest
  • hospital mortality
  • inpatients
  • intensive care units
  • length of stay
  • mortality
  • natural language processing
  • nurses
  • outcome
  • patient risk
  • physicians
  • predictive value
  • preventable death
  • readmission rates
  • risk
  • sepsis
  • simulation
  • support tools
  • surveys
  • testing
  • tool
Country: United States
State: New York
Zip Code: 10032
UI: 20183392
Project Status: Ongoing
Record History: ('2020: Project extended to 2023. ',)